Speech Recognition with State-based Nearest Neighbour Classifiers.
Abstract
We present a system that uses nearest neighbour classification
on the state level of the hidden Markov model. Common speech
recognition systems nowadays use Gaussian mixtures with a
very high number of densities. We propose to carry this idea
to the extreme, such that each observation is a prototype of its
own. This approach is well-known and widely used in other areas
of pattern recognition and has some immediate advantages
over other classification approaches, but has never been applied
to speech recognition. We evaluate the proposed method on the
SieTill corpus of continuous digit strings and on the large vocabulary
EPPS English task. It is shown that nearest neighbour
outperforms conventional systems when training data is sparse.
Index Terms: automatic speech recognition, nearest neighbour
classification, kernel densities.